Advances in machine learning are making security systems easier to train and more flexible in dealing with changing conditions, but not all use cases are benefitting at the same rate

Advances in machine learning are making security systems easier to train and more flexible in dealing with changing conditions, but not all use cases are benefitting at the same rate.

Machine learning, and artificial intelligence, has been getting a lot of attention lately and there's a lot of justified excitement about the technology.

One of the side effects is that pretty much everything is now being relabeled as "machine learning," making the term extremely difficult to pin down. Just as the word "cloud" has come to mean pretty much anything that happens online, so "artificial intelligence" is rapidly moving to the point where almost anything involving a computer is getting that label slapped on it.

"There is also a lot of hype," said Anand Rao, innovation lead for US analytics at PricewaterhouseCoopers LLC. "People talk about AI becoming super intelligent and will take over humanity and human decision making so on."

One common security tasks is to determine whether newly-downloaded or installed applications are malicious. The traditional approach is a very basic expert system -- does the application's signature match that of known malware?

The downside of this standard antivirus approach, however, is that it needs to be updated constantly as new malware shows up, and it is extremely brittle. A piece of malware that has only minor modifications in it can easily evade detection.